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Update app.py
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app.py
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import os
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from openai import OpenAI
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import requests
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import gradio as gr
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client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
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payload =
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"k": k
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}
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response = requests.post(url, json=payload, headers=headers)
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response.raise_for_status()
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return response.json()
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except requests.exceptions.RequestException as e:
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return f"An error occurred: {e}"
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{"role": "system", "content": "You are a helpful assistant. Answer the question based on the provided context."},
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{"role": "user", "content": prompt}
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]
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"An error occurred while querying OpenAI: {e}"
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# Function to perform vector search and format results
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def vector_search(query):
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results = search_document(query)
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if isinstance(results, str): # Error occurred
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return results
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if not isinstance(results, dict) or 'results' not in results:
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return "Unexpected format in vector database response."
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content = result['metadata']['content']
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source = f"Source {i}: {result['metadata'].get('source', 'Unknown source')}, page {result['metadata'].get('page', 'Unknown page')}"
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metadata = ", ".join([f"{k}: {v}" for k, v in result['metadata'].items() if k != 'content'])
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formatted_results += f"{source}\nMetadata: {metadata}\nContent: {content}\n\n"
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search_results = vector_search(question)
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Question: {question}
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Answer:"""
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answer_output = gr.Textbox(label="OpenAI Answer")
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query_button = gr.Button("Get Answer")
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demo.launch()
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import gradio as gr
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import requests
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API_URL = "http://154.12.226.68:8000"
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def search_document(index_name, query, k):
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url = f"{API_URL}/search/{index_name}"
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payload = {"text": query, "k": k}
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headers = {"Content-Type": "application/json"}
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response = requests.post(url, json=payload, headers=headers)
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results = response.json()
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formatted_results = []
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for result in results.get('results', []):
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metadata = result.get('metadata', {})
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formatted_result = f"Source: {metadata.get('source', 'Unknown')}\n"
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formatted_result += f"Page: {metadata.get('page', 'Unknown')}\n"
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formatted_result += f"Content: {metadata.get('content', 'No content available')}\n"
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formatted_result += f"Distance: {result.get('distance', 'Unknown')}\n"
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formatted_results.append(formatted_result)
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return "\n\n".join(formatted_results)
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def qa_document(index_name, question, k):
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url = f"{API_URL}/qa/{index_name}"
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payload = {"text": question, "k": k}
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headers = {"Content-Type": "application/json"}
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response = requests.post(url, json=payload, headers=headers)
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result = response.json()
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answer = result.get('answer', 'No answer available')
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sources = result.get('sources', [])
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formatted_sources = []
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for source in sources:
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formatted_source = f"Source: {source.get('source', 'Unknown')}\n"
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formatted_source += f"Relevance Score: {source.get('relevance_score', 'Unknown')}"
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formatted_sources.append(formatted_source)
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formatted_result = f"Answer: {answer}\n\nSources:\n" + "\n\n".join(formatted_sources)
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return formatted_result
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with gr.Blocks() as demo:
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gr.Markdown("# Document Search and Question Answering System")
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index_name = gr.Textbox(label="Index Name", value="default")
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with gr.Tab("Search"):
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search_input = gr.Textbox(label="Search Query")
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search_k = gr.Slider(1, 10, 5, step=1, label="Number of Results")
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search_button = gr.Button("Search")
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search_output = gr.Textbox(label="Search Results", lines=10)
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search_button.click(search_document, inputs=[index_name, search_input, search_k], outputs=search_output)
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with gr.Tab("Question Answering"):
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qa_input = gr.Textbox(label="Question")
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qa_k = gr.Slider(1, 10, 5, step=1, label="Number of Contexts to Consider")
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qa_button = gr.Button("Ask Question")
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qa_output = gr.Textbox(label="Answer and Sources", lines=10)
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qa_button.click(qa_document, inputs=[index_name, qa_input, qa_k], outputs=qa_output)
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demo.launch()
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